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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

Question 1

A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A. Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
B. Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()
C. Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
D. Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()

Question 2

A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
B. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
C. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
D. The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.

Question 3

33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A. raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
B. raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
C. raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))
D. raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))

Question 4

A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:

A. Add the option wholetext=False to the text() function
B. Add the option lineSep=", " to the text() function
C. Add the option lineSep='\n' to the text() function
D. Add the option wholetext=True to the text() function

Question 5

A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A. If a SparkSession already exists, this code will return the existing session instead of creating a new one.
B. The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
C. A new SparkSession is created every time the getOrCreate() method is invoked.
D. A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.

Solutions: